DETECTION THEORY APPROACH TO MULTICHANNEL PATTERN LOCATION

We propose and assess new algorithms for detecting and locating an obj ect in multichannel images. These algorithms are optimal for additive Gaussian noise and maximize the likelihood of the observed images. We consider two cases, in which the illumination of the target and the va riance of the noise in each channel are either known or unknown. We sh ow that in the latter case the algorithm provides accurate estimates o f variance and luminance. These algorithms can be viewed as postproces sed versions of the correlation of a reference with the scene image in each channel. (C) 1997 Optical Society of America.